In genetic association studies, a single marker is often associated with multiple, correlated phenotypes (e.g., obesity and cardiovascular disease, or nicotine dependence and lung cancer). A pervasive question is then whether that marker exerts independent effects on all phenotypes. In this paper, we address this question by assessing whether there is a genetic effect on one phenotype that is not mediated through the other ones, so called direct genetic effect. Answering such question may represent an important step in the elucidation of the underlying biological mechanism. Under rather restrictive conditions, such direct genetic effects are known to be estimable by standard regression methods. Under more lenient conditions, in a prospective or unmatched case-control study, these effects can be estimated by using a previously proposed G-estimation method (Vansteelandt [2009] Epidemiology 20, 851-860). The present paper extends this method to matched case-control studies, and investigates the conditions under which this extension is valid. We illustrate the method on data from a matched case-control study, which we use to elucidate the pathway implications of a detected association between myocardial infarction and a genetic locus in the chromosomal region of the FTO gene.

Direct genetic effects and their estimation from matched case-control data

BERNARDINELLI, LUISA
2012-01-01

Abstract

In genetic association studies, a single marker is often associated with multiple, correlated phenotypes (e.g., obesity and cardiovascular disease, or nicotine dependence and lung cancer). A pervasive question is then whether that marker exerts independent effects on all phenotypes. In this paper, we address this question by assessing whether there is a genetic effect on one phenotype that is not mediated through the other ones, so called direct genetic effect. Answering such question may represent an important step in the elucidation of the underlying biological mechanism. Under rather restrictive conditions, such direct genetic effects are known to be estimable by standard regression methods. Under more lenient conditions, in a prospective or unmatched case-control study, these effects can be estimated by using a previously proposed G-estimation method (Vansteelandt [2009] Epidemiology 20, 851-860). The present paper extends this method to matched case-control studies, and investigates the conditions under which this extension is valid. We illustrate the method on data from a matched case-control study, which we use to elucidate the pathway implications of a detected association between myocardial infarction and a genetic locus in the chromosomal region of the FTO gene.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/438048
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